The Complicated Relationship Between Assists and Turnovers

NBA 2023-24 Season: Correlation = 0.64. Playmakers who create more assists also tend to commit more turnovers

30DayChartChallenge
Data Visualization
R Programming
2025
Exploring the nuanced relationship between playmaking and ball security in the NBA, this visualization reveals how elite players navigate the trade-off between generating assists and limiting turnovers.
Author

Steven Ponce

Published

April 15, 2025

Figure 1: Scatter plot showing NBA 2023-24 season data comparing assists versus turnovers per 36 minutes (correlation = 0.64). Players are divided into four categories: Efficient Playmakers, High-Risk Playmakers, Low-Usage Ball Handlers, and Turnover-Prone. Key players like Haliburton, Wembanyama, and James are labeled, illustrating the complicated relationship between playmaking and ball security.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
  tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  hoopR,          # Access Men's Basketball Play by Play Data
  paletteer,      # Comprehensive Collection of Color Palettes
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with ggplot2
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
# Get player stats for 2023-2024 season
nba_players <- load_nba_player_box(seasons = 2024)

3. Examine the Data

Show code
glimpse(nba_players)
skim(nba_players)

4. Tidy Data

Show code
### |- Tidy ----
# Calculate aggregate stats (assists and turnovers)
player_stats <- nba_players |>  
  group_by(athlete_display_name, athlete_position_name) |>
  summarize(
    games = n(),
    minutes = sum(minutes, na.rm = TRUE),
    assists = sum(assists, na.rm = TRUE),
    turnovers = sum(turnovers, na.rm = TRUE),
    ast_per_36 = (assists / minutes) * 36,
    tov_per_36 = (turnovers / minutes) * 36,
    ast_to_ratio = assists / pmax(turnovers, 0.5),  
    .groups = "drop"
  ) |>
  filter(
    minutes >= 500,
    !is.na(ast_per_36),
    !is.na(tov_per_36),
    is.finite(ast_per_36),  
    is.finite(tov_per_36)
  ) |> 
  mutate(
    high_ast = ast_per_36 > median(ast_per_36),
    high_tov = tov_per_36 > median(tov_per_36),
    playmaker_type = case_when(
      high_ast & !high_tov ~ "Efficient Playmakers",
      high_ast & high_tov ~ "High-Risk Playmakers",
      !high_ast & !high_tov ~ "Low-Usage Ball Handlers",
      !high_ast & high_tov ~ "Turnover-Prone"
    )
  )

# Correlation
correlation <- cor(
  player_stats$ast_per_36, 
  player_stats$tov_per_36, 
  use = "complete.obs"
  )

# Label df
elite_players <- player_stats |>
  filter(
    minutes > 1500 & (
      # Elite point guards and playmakers
      ast_per_36 > quantile(player_stats$ast_per_36, 0.95) |
        # Players with unusual assist/turnover combos
        (ast_per_36 > 7 & tov_per_36 < 1.5) |
        tov_per_36 > quantile(player_stats$tov_per_36, 0.95) |
        # Super-efficient playmakers
        (ast_per_36 > 6 & ast_to_ratio > 4) |
        # manually selected a few stars
        athlete_display_name %in% c(
          "LeBron James", "Nikola Jokic", "Chris Paul", 
          "Victor Wembanyama", "Tyrese Haliburton", "Joel Embiid"
        )
    )
  ) |>
  # Top N 
  slice_max(order_by = minutes, n = 15)

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "Efficient Playmakers" = "#009E73", 
    "High-Risk Playmakers" = "#171738",  
    "Low-Usage Ball Handlers" = "#593C8F", 
    "Turnover-Prone" = "#DB5461" 
  )
)

### |-  titles and caption ----
# text
title_text    <- str_wrap("The Complicated Relationship Between Assists and Turnovers",
                          width = 60) 

subtitle_text <- str_glue(
  "NBA 2023-24 Season: Correlation =  { round(correlation, 2) }<br>
  Playmakers who create more assists also tend to commit more turnovers"
)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 15,
  source_text =  "ESPN via { hoopR } package" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(

    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),

    # Grid elements
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "gray92"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$text, size = rel(0.8)),
    legend.text = element_text(family = fonts$text, size = rel(0.7)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----
p <- ggplot(player_stats, 
       aes(x = ast_per_36, y = tov_per_36, color = playmaker_type)
) +
  # Geoms
  geom_point(
    aes(size = minutes), 
    alpha = 0.35
    ) +
  geom_hline(
    yintercept = median(player_stats$tov_per_36), 
    linetype = "dotted", 
    color = "gray50",
    alpha = 0.7
  ) +
  geom_vline(
    xintercept = median(player_stats$ast_per_36), 
    linetype = "dotted", 
    color = "gray50",
    alpha = 0.7
  ) +
  geom_smooth(
    method = "lm", 
    formula = y ~ x,
    se = FALSE, 
    color = "gray50", 
    linetype = "dashed",
    alpha = 0.5,
    linewidth = 0.5
  ) +
  geom_text_repel(
    data = elite_players,
    aes(label = athlete_display_name),
    size = 3, 
    max.overlaps = 10,
    box.padding = 0.3,
    point.padding = 0.2,
    segment.color = "gray30",
    min.segment.length = 0.2,
    force = 5, 
    seed = 123
  ) +
  # Scales
  scale_x_continuous(
    breaks = seq(0, 12, by = 2),
    limits = c(0, max(player_stats$ast_per_36) * 1.05)
  ) +
  scale_y_continuous(
    breaks = seq(0, 5, by = 1),
    limits = c(0, max(player_stats$tov_per_36) * 1.05)
  ) +
  scale_size_continuous(
    name = "Total Minutes Played", 
    range = c(1.5, 7),
    breaks = c(1000, 2000, 3000)
  ) +
  scale_color_manual(
    values = colors$palette
    ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Assists Per 36 Minutes",
    y = "Turnovers Per 36 Minutes",
  ) +
  # Annotate
  annotate(
    "text", 
    x = 11.5, 
    y = 1.8,
    label = "Median assists", 
    fontface = "italic", 
    size = 2.8, 
    color = "gray40",
    hjust = 0.5
  ) +
  annotate(
    "text", 
    x = 3, 
    y = 4.2,
    label = "Median turnovers", 
    fontface = "italic", 
    size = 2.8, 
    color = "gray40",
    hjust = 0,
    angle = 90
  ) +
  annotate(
    "text", 
    x = 11, 
    y = 3.65,
    label = "2:1 Assist-to-Turnover Ratio", 
    fontface = "italic", 
    size = 2.5, 
    color = "gray30",
    angle = 27 
  ) +
  annotate(
    "text", 
    x = 7.5,
    y = 0.2,
    label = "Efficient Playmakers", 
    fontface = "bold", 
    size = 3.5, 
    color = colors$palette[1]
  ) +
  annotate(
    "text", 
    x = 10,
    y = 4.05,
    label = "High-Risk Playmakers", 
    fontface = "bold", 
    size = 3.5, 
    color = colors$palette[2]
  ) +
  annotate(
    "text", 
    x = 1,
    y = 0.2,
    label = "Low-Usage Ball Handlers", 
    fontface = "bold", 
    size = 3.5, 
    color = colors$palette[3]
  ) +
  annotate(
    "text", 
    x = 1,
    y = 4.05,
    label = "Turnover-Prone", 
    fontface = "bold", 
    size = 3.5, 
    color = colors$palette[4]
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.5),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.5,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.6),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 5, b = 5)
    ),
  )

7. Save

Show code
### |-  plot image ----  

save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 15, 
  width = 8, 
  height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 ggrepel_0.9.6   paletteer_1.6.0
 [5] hoopR_2.1.0     scales_1.3.0    skimr_2.1.5     janitor_2.2.0  
 [9] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[17] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[21] ggplot2_3.5.1   tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] rematch2_2.1.2      rlang_1.1.4         magrittr_2.0.3     
 [4] snakecase_0.11.1    furrr_0.3.1         compiler_4.4.0     
 [7] mgcv_1.9-1          systemfonts_1.1.0   vctrs_0.6.5        
[10] rvest_1.0.4         pkgconfig_2.0.3     fastmap_1.2.0      
[13] magick_2.8.5        utf8_1.2.4          promises_1.3.0     
[16] rmarkdown_2.29      markdown_1.13       tzdb_0.4.0         
[19] ps_1.8.1            ragg_1.3.3          xfun_0.49          
[22] jsonlite_1.8.9      later_1.3.2         parallel_4.4.0     
[25] R6_2.5.1            stringi_1.8.4       parallelly_1.43.0  
[28] Rcpp_1.0.13-1       knitr_1.49          base64enc_0.1-3    
[31] pacman_0.5.1        Matrix_1.7-0        splines_4.4.0      
[34] timechange_0.3.0    tidyselect_1.2.1    rstudioapi_0.17.1  
[37] yaml_2.3.10         codetools_0.2-20    websocket_1.4.2    
[40] curl_6.0.0          processx_3.8.4      listenv_0.9.1      
[43] lattice_0.22-6      withr_3.0.2         evaluate_1.0.1     
[46] future_1.34.0       RcppParallel_5.1.10 xml2_1.3.6         
[49] pillar_1.9.0        renv_1.0.3          generics_0.1.3     
[52] rprojroot_2.0.4     chromote_0.4.0      hms_1.1.3          
[55] commonmark_1.9.2    munsell_0.5.1       globals_0.16.3     
[58] glue_1.8.0          tools_4.4.0         data.table_1.16.2  
[61] grid_4.4.0          colorspace_2.1-1    nlme_3.1-164       
[64] repr_1.1.7          cli_3.6.3           textshaping_0.4.0  
[67] rsvg_2.6.1          fansi_1.0.6         svglite_2.1.3      
[70] gtable_0.3.6        digest_0.6.37       progressr_0.15.1   
[73] gifski_1.32.0-1     htmlwidgets_1.6.4   farver_2.1.2       
[76] htmltools_0.5.8.1   lifecycle_1.0.4     httr_1.4.7         
[79] gridtext_0.1.5

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2025_15.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
    • ESPN via { hoopR } package: hoopR
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